emraresearch.org

A Sociological Analysis of Structural Racism in Student List Products

Ozan Jaquette

UCLA

Karina Salazar

University of Arizona

ozanj.github.io/student_list_hsls/slides/student_list.html

emraresearch.org

Introduction

emraresearch.org

The market for college access

A two-sided matching problem in which market allocates students to colleges (Hoxby, 1997; Hoxby, 2009)

Barriers to efficient market:
- transportation costs; information costs
Students
- Goal: want to attend college
- Information problem: Don’t know where they will be admitted, how much it will cost
Colleges want to enroll students
- Enrollment goals: academic profile; revenue; diversity; internal constituents
- Information problem: Don’t know who/where the “good” students are, how to contact them

Matchmaking

“Inquiries” (student as first contact)
Students send test scores or fill out inquiry form
The enrollment problem
- Most colleges cannot survive/thrive solely from students who reach out on their own
- Must find desirable prospects who can be convinced to apply/enroll

emraresearch.org

Student lists products

“Student list” products are a matchmaking intermediary that connects colleges to prospects

Third-party vendors obtain data and contact info about prospecs (e.g., Testing orgs, search engines)
Vendors sell contact information of prospects to colleges looking for students
- Colleges choose prospect profiles by filtering on search filters (e.g., zip code, test score)

Policy concerns about student list products

Problem with underlying products
- Search filters incorporate “racialized inputs” (Norris, 2021) that systematically disadvantage underrepresented students of color
Problem with utilization of products
- University administrators may choose combinations of search filters that result in racial exclusion

Research questions

What is relationship between search filters and racial composition of included vs excluded students?
How do public universities use racialized search filters in concert with other search filters when purchasing student lists?
What is observed racial composition of student list purchases that utilize racialized search filters in concert with other search filters?

emraresearch.org

Research overview

Research questions

What is relationship between search filters and racial composition of included vs excluded students?
- Reconstruct College Board student list product using nationally representative sample of 9th graders from 2009 (NCES High School Longitudinal Survey)
- Simulate which students are included versus excluded when certain search filters are utilized

How do public universities use racialized search filters in concert with other search filters when purchasing student lists?
- Analyze 830 student lists purchased by 14 public universities, collected via public records requests

What is observed racial composition of student list purchases that utilize racialized search filters in concert with other search filters?
- Analyze targeted student list purchases, where we obtained both the order details and deidentified prospect-level data

emraresearch.org

Background & Literature

emraresearch.org

How student lists fit into recruiting undergraduates

Prospects

Population of desirable potential students

Leads

Prospects with contact info

Inquiries

Prospects who have contacted the institution
- Institution as first contact (leads)
- Student as first contact

Interventions along the funnel

Convert prospects to leads
- purchase student lists
  - list-based lead generation
  - based on direct mail model
Convert leads/inquiries to applicants
- Email, mail, targeted social media
Convert admits to enrolles
- Financial aid packages

**The enrollment funnel**

Source: pngwing.com

emraresearch.org

Scholarship on recruiting from sociology

Enrollment funnel: prospects >> leads >> inquires >> applicants >> admits >> enrolled

Most scholarship on enrollment management focuses on latter stages (admissions, financial aid)
Body of research in sociology that analyzes recruiting “in the wild”

Recruiting from perspective of high school students (Holland, 2019)

Underrepresented students sensitive to feeling “wanted” by colleges

Connections between high schools and colleges from organizational behavior perspective

Off-campus recruiting visits indicate a network tie and enrollment priorities
recruiting from perspective of private college (Stevens, 2007), private HS counselors (Khan, 2011)
Recruiting visits by public research univs (e.g., Salazar, Jaquette, and Han, 2021; and Salazar, 2022)

Recruiting at open-access PSIs for adults (e.g., and Cottom, 2017; and Posecznick, 2017)

For-profits have demand in Black/Latinx communities because traditional colleges ignore them

Scholarship assumes that recruiting is something done wholly done by individual colleges

Third-party products and vendors structure recruiting behavior by colleges
How do products incorporate structural inequality? How are they utilized by colleges?

emraresearch.org

College Board and ACT student list products

Sources of student list data

Test takers (e.g., PSAT, SAT, AP), pre-test questionnaire (demographics, preferences)
More recently, from college search engines (e.g., College Board Big Future)
Students can opt in or out

What information does a list contain (College Board template)

Contact, demographic, college preferences, limited academic achievement

Pricing

Historically, price-per-prospect (e.g., $0.50 per name); now a subscription model

Buying lists: “search filters” control which prospects included in purchase

Commonly used search filters (Link to ACT filters)
- e.g., HS GPA, test score range, gender, race, geography (e.g., state), enrollment probability
Salazar, Jaquette, and Han (2022) categorizes College Board search filters into four buckets:
- Geographic
- Academic
- Demographic
- Student preferences

emraresearch.org

Filters used in 830 lists purchased by 14 public universities

emraresearch.org

College Board lists and student outcomes

Howell, Hurwitz, Mabel et al. (2021)

also see: Smith, Howell, and Hurwitz (2022) for effect of university purchasing profile on college choice

emraresearch.org

Conceptual Framework

emraresearch.org

Sociology of race

Selection devices allocate individuals to categories based on input factors (Hirschman and Bosk, 2020)

Standardized selection devices
- decisions based on mathematical function in which input values determine outcome value
- “actuarial” selection devices predict outcomes based on analysis of past cases
Discretionary selection devices
- Decisions incorporate judgment of individual evaluators (e.g., holistic admissions)
- Student list products are discretionary selection devices

Standardized selection devices and racial inequality

May reduce racial inequality if source of inequality is bias from individual decision-maker
Do not reduce inequality due to structural racism (Bonilla-Silva, 1997)
- Structural racism is “systematic racial bias embedded in the ‘normal’ functions of laws and social relations,” whereby processes viewed as neutral or common-sense systematically disadvantage marginalized groups (Tiako, South, and Ray, 2021, p. 1143)

Discretionary selection devices and racial inequality

Sensitive to individual bias from decision-makers and inputs related to race

emraresearch.org

Racialized inputs

Norris (2021)

reconstructs inputs used by Moody’s city government credit rating algorithm
Racialized inputs: “theoretically and empirically correlated with historical racial disadvantage” (p. 5)
“colorblind” selection devices reproduce racial inequality by using seemingly neutral inputs that are systematically related to race because marginalized groups have been historically excluded from input

Geography inputs

US racially segregated due to historic and current laws/policies/practices
Zip code an input in algorithm that predicts crime recidivism (O’Neil, 2016; Benjamin, 2019)
- Zip code a search filter on CB/ACT student list products
- Ruffalo Noel Levitz algorithm recommends which zip codes to buy names
Geodemographic classifications classify localities by consumer behavior (Leyshon and Thrift, 1999)
- CB Geodemographic filters classify census tracts by past college enrollment (College Board, 2011)

Inputs based on predictive analytics

Analyze determinants of an outcome in past cases, use results as an input to select future cases (e.g., ACT “enrollment predictor” filter)

emraresearch.org

RQ1: relationship between filters and racial composition

Standardized college entrance exams and AP exams as racialized inputs

Racial/ethnic differences in test-taking and test scores a function of historic/contemporary school segregation, differences in school funding and access to curricula (e.g., Reardon, Kalogrides, and Shores, 2019; Rodriguez and Hernandez-Hamed, 2020)

P1: The condition of taking standardized assessments is associated with racial disparities in who is included versus excluded in student list products.

P2: As test score threshold increases, proportion of underrepresented minority students included in student lists declines relative to proportion who are excluded.

P3. As purchases filter on more affluent zip codes, the proportion of underrepresented minority students included in student lists declines relative to proportion excluded.

P4. Filtering on smaller geographic localities is associated with greater racial disparities in included vs. excluded than filtering on larger geographic localities.

Filtering on multiple racialized inputs has compounding effect on racial inequality

emraresearch.org

Methods

emraresearch.org

Data

emraresearch.org

PRESENTATION STUFF TO DO

background section;
- replace current enrollment funnel w/ the one from here (might ask crystal to do this): https://ticas.org/wp-content/uploads/2022/09/Geodemographics-of-Student-List-Purchases_A-First-Look.pdf
on results slides, for RQ1
- look to see if slide title and sub-title makes sense
  - like I tried to indicate in slide titles which proposition being tested
- modify some graphs to make sure they pit on page
- I may have fucked up a couple of slide titles/contents somehow. my bad homie…
results slides for RQ3
- for the four deep dive purchases
  - add text at bottom of slide that indicates the filters specified in the orders being analyzed (e.g., the segment one currently just says “Segment” but we should show that it is segment used in concert w/ these other filters)

emraresearch.org

HSLS09 & Student List Project

High School Longitudinal Study of 2009 (HSLS09)

Students who completed 2012/2013 survey follow-ups and obtained high school transcript data
- Unweighted analysis sample (n=16,530)
- Weighted analysis sample represent approximately 4.2 million U.S. 9th graders in 2009

Student List Project

Issued public records requests for student list data (2016-2020) to all public universities in four states (CA, IL, MN, TX)
Target student list vendors: College Board, ACT
For each purchased list, sought two pieces of data
- “Order summary” specifying search filter criteria (LINK)
- De-identified prospect-level student list (LINK)
Funded by Joyce Foundation and Kresge Foundation

emraresearch.org

Research design

emraresearch.org

Variables & Analyses

RQ1. What is relationship between search filters and racial composition of included vs excluded students?

Data: HSLS09
Dependent Variable: student race/ethnicity
Independent Variables: measures of student list academic and geographic filters
- Dichotomous and score threshold measures of SAT, PSAT, AP (and GPA)
  - Note: HSLS measures for SAT includes ACT, and PSAT includes PreACT
- Economic measures of students’ zipcode, county, CBSA from ACS
Analyses: inferential descriptive statistics (comparing two propoportions)
- Racial composition of included vs excluded prospects when particular filters utilized

RQ2. How do public universities use racialized search filters in concert with other search filters when purchasing student lists?

Data: order summaries from 830 College Board student lists purchased by 14 public universities
Analyses: descriptive statistics of search filters specified in orders

RQ3. What is observed racial composition of student list purchases that utilize racialized search filters in concert with other search filters?

Data: de-identified prospect-level student list data; incorporate ACS data for comparisons
Analyses: descriptive statistics of targeted student list purchases

emraresearch.org

Results

emraresearch.org

RQ1

emraresearch.org

Test Takers

P1: Racial disparities in test-taking

emraresearch.org

Test thresholds

P2: SAT, PSAT score thresholds and racial composition

emraresearch.org

Test thresholds

P2: AP sScore thresholds and racial composition

emraresearch.org

Geography

P3: Zip code affluence and racial composition

emraresearch.org

Geography

Zip vs county, affluence percentiles within CBSA

emraresearch.org

Academic & Geographic

GPA (3.0+) and SAT or PSAT (across score thresholds)

emraresearch.org

Academic & Geographic

GPA (3.0+), PSAT (150+) or SAT (1050+), and Zip (across income thresholds)

emraresearch.org

Academic & Geographic

GPA (3.0+) and AP (across score thresholds)

emraresearch.org

RQ2

emraresearch.org

Broad patterns

Filters used in order purchases

emraresearch.org

Combination of filters

Filter combos used in order purchases

Research			MA/doctoral
Filters	Count	Percent	Filters	Count	Percent
HS grad class, GPA, SAT, PSAT, Rank, State, Race	39	10%	HS grad class, GPA, SAT, Zip code	206	45%
HS grad class, PSAT, State	27	7%	HS grad class, GPA, PSAT, Zip code	145	32%
HS grad class, GPA, PSAT, State, Race	20	5%	HS grad class, SAT, State	31	7%
HS grad class, PSAT, State, Low SES	20	5%	HS grad class, GPA, SAT, PSAT, Zip code	28	6%
HS grad class, GPA, PSAT, State	17	5%	HS grad class, GPA, SAT, State	7	2%
HS grad class, GPA, SAT, State	16	4%	HS grad class, SAT, Geomarket	6	1%
HS grad class, GPA, AP score, Geomarket	15	4%	HS grad class, GPA, SAT, County	5	1%
HS grad class, GPA, SAT, PSAT, State, Segment, Gender	13	3%	HS grad class, GPA, SAT, PSAT, County	4	1%
HS grad class, PSAT, Geomarket	12	3%	HS grad class, GPA, PSAT, State	2	0%
HS grad class, SAT, State, Low SES, College size	11	3%	HS grad class, SAT, Geomarket, College type	2	0%

emraresearch.org

RQ3

emraresearch.org

Characteristics by filters

Prospect characteristics across individual filter criteria

			Academic					Geographic					Demographic
		All domestic	GPA	PSAT	SAT	HS rank	AP score	Zip code	State	Geomarket	Segment	CBSA	Race	Gender
Total		3,547,620	1,101,266	1,812,447	971,237	146,660	75,479	165,924	1,173,678	1,056,951	186,519	146,313	279,626	39,546
Location
	% In-state	38	62	30	54	83	42	98	48	17	15	4	59	6
	% Out-of-state	62	38	70	46	17	58	2	52	83	85	96	41	94
Race/ethnicity
	% White	48	45	50	47	51	17	43	42	57	51	53	25	47
	% Asian	16	15	17	15	10	7	13	18	13	27	28	5	38
	% Black	5	7	4	7	8	17	8	5	4	3	2	11	1
	% Latinx	21	24	19	22	23	46	27	24	16	11	8	46	6
	% AI/AN	1	1	1	0	1	1	1	1	0	0	0	2	0
	% NH/PI	0	0	0	0	0	1	0	0	0	0	0	0	0
	% Multiracial	5	5	5	5	5	10	4	6	5	5	5	9	5
	% Other	0	0	0	0	0	0	0	0	0	0	0	0	0
	% No response	4	3	3	3	2	1	4	3	4	3	3	2	3
	% Missing	0	0	1	0	0	0	1	1	1	0	0	0	0
Gender
	% Male	34	19	37	18	0	3	46	24	48	6	0	11	0
	% Female	36	23	40	20	1	15	54	27	52	9	0	12	33
	% Other	0	0	0	0	0	0	0	0	0	0	0	0	0
	% Missing	30	58	22	63	99	82	0	49	0	85	1	77	67
Household income
	Median income	$107K	$105K	$108K	$105K	$99K	$90K	$97K	$105K	$107K	$130K	$135K	$94K	$127K
Locale
	% City	27	27	27	26	26	31	31	30	23	24	22	29	26
	% Suburban	44	47	44	48	53	40	42	42	46	54	57	47	49
	% Rural - Fringe	22	20	22	20	15	23	19	22	23	19	19	19	23
	% Rural - Distant	6	6	5	6	6	5	7	6	6	2	1	6	2
	% Rural - Remote	1	0	1	0	0	0	1	1	1	0	0	0	0
	% Missing	0	0	0	0	0	0	0	0	0	0	0	0	0

emraresearch.org

Geodemographic segment filters

Filter by neighborhood segments

2011 D+ Cluster	SAT Math	SAT CR	Going Out of State	Percent NonWhite	Need Financial Aid	Med Income
51	546.00	533.00	32%	30%	57%	$95,432
52	480.00	470.00	30%	58%	71%	$63,578
53	561.00	544.00	32%	50%	55%	$92,581
54	458.00	443.00	25%	83%	76%	$38,977
55	566.00	565.00	52%	24%	63%	$71,576
56	420.00	411.00	29%	93%	66%	$35,308
57	541.00	519.00	52%	47%	43%	$67,394
58	533.00	489.00	28%	87%	69%	$68,213
59	561.00	562.00	52%	24%	74%	$54,750
60	589.00	590.00	63%	37%	36%	$104,174
61	585.00	567.00	51%	30%	40%	$123,858
62	596.00	595.00	67%	24%	72%	$59,824
63	548.00	541.00	39%	23%	65%	$69,347
64	466.00	466.00	48%	34%	29%	$49,829
65	440.00	433.00	23%	93%	78%	$45,081
66	499.00	492.00	20%	12%	76%	$50,453
67	519.00	501.00	27%	53%	59%	$60,960
68	552.00	558.00	52%	35%	65%	$57,902
69	534.00	521.00	37%	19%	65%	$88,100
70	613.00	598.00	65%	29%	61%	$86,381
71	405.00	408.00	39%	97%	68%	$42,661
72	399.00	397.00	31%	87%	47%	$32,708
73	528.00	514.00	29%	42%	62%	$90,849
74	433.00	435.00	29%	84%	79%	$44,065
75	459.00	457.00	28%	85%	72%	$50,421
76	514.00	509.00	27%	38%	64%	$61,332
77	502.00	492.00	26%	18%	75%	$62,372
78	594.00	578.00	56%	26%	39%	$134,400
79	550.00	551.00	57%	32%	74%	$40,909
80	534.00	527.00	39%	39%	65%	$49,877
81	491.00	483.00	27%	57%	72%	$63,030
82	496.00	491.00	29%	21%	75%	$53,465
83	500.00	490.00	19%	26%	71%	$49,335
Total	512.00	502.00	32%	43%	65%	$70,231

emraresearch.org

Filter by high school segments

2011 D+ Cluster	SAT Math	SAT CR	Going Out of State	Percent NonWhite	Need Financial Aid	Med Income
51	462.00	457.00	14%	33%	68%	$40,918
52	489.00	496.00	81%	99%	77%	$64,730
53	471.00	484.00	28%	38%	62%	$60,833
54	376.00	371.00	33%	96%	38%	$38,146
55	489.00	481.00	39%	46%	44%	$71,845
56	536.00	508.00	73%	43%	49%	$63,967
57	434.00	435.00	29%	82%	79%	$48,301
58	592.00	577.00	51%	27%	32%	$104,509
59	499.00	489.00	19%	18%	74%	$47,685
60	523.00	549.00	23%	30%	33%	$70,175
61	485.00	370.00	33%	89%	9%	$61,385
62	474.00	473.00	34%	92%	67%	$55,515
63	440.00	427.00	28%	86%	72%	$49,238
64	606.00	542.00	37%	89%	57%	$81,911
65	515.00	503.00	28%	43%	65%	$72,692
66	498.00	515.00	37%	37%	73%	$60,272
67	526.00	546.00	48%	41%	69%	$71,279
68	541.00	540.00	41%	26%	62%	$79,260
69	390.00	395.00	36%	92%	74%	$43,391
70	595.00	581.00	56%	33%	48%	$105,721
71	400.00	412.00	57%	98%	80%	$43,137
72	528.00	544.00	35%	25%	64%	$70,018
73	451.00	438.00	24%	89%	76%	$48,406
74	654.00	579.00	76%	80%	46%	$59,089
75	514.00	502.00	31%	20%	71%	$72,850
76	600.00	584.00	72%	50%	28%	$90,265
77	595.00	508.00	64%	75%	39%	$39,490
78	473.00	468.00	48%	43%	22%	$56,703
79	594.00	585.00	61%	26%	71%	$65,180
Total	514.00	502.00	32%	44%	65%	$70,223

emraresearch.org

Segment filter prospects by metro

emraresearch.org

Segment filter prospects interactive map

emraresearch.org

Women in STEM

Women in STEM prospects by metro

emraresearch.org

Targeting URM students

Race and ethnicity variables, aggregated vs. alone

emraresearch.org

Purchased profiles for students of color by metro

emraresearch.org

Purchased profiles for students of color interactive map

emraresearch.org

Zip code & test score filters

Los Angeles prospects from top income decile zip codes

emraresearch.org

Discussion

emraresearch.org

Future research on student list products

Improve this paper enough to get published

How to motivate targeted analyses about how universities utilize student list products

Papers on College Board filters that create geographic borders from analyses of past college enrollment

Geomarket filters slice metropolitan areas into several education markets
Geodemography filters categorize neighborhoods and high schools into clusters

Explore link between student list products and extension of credit

Parallel origins of consumer credit reports and student lists (Hoxby, 2009; Leyshon and Thrift, 1999)
- Reduce information assymetry by providing businesses information about customers
- Business model moves from evaluation of applicants to active courtship of desired customers
Consumer credit reports regulated under Fair Credit Reporting Act and Consumer Finance Protection Act because they lead to extension of credit
Student list products systematically lead to student loans

Regulatory policy

Students list products are powerful tools, can be used for good or bad
Should policymakers continue tolerating products likely to do harm because they also do good?

emraresearch.org

References

[1] R. Benjamin. Race after technology: Abolitionist tools for the new Jim code. Medford, MA: Polity, 2019. ISBN: 9781509526390 (hardback) 9781509526406 (paperback).

[2] E. Bonilla-Silva. “Rethinking racism: Toward a structural interpretation”. In: American Sociological Review 62.3 (1997), pp. 465-480. ISSN: 0003-1224. DOI: 10.2307/2657316. URL: <Go to ISI>://WOS:A1997XG45300007.

[3] College Board. Segment Analysis Service: An educationally relevant geodemographic tagging service. Tech. rep. College Board, 2011. URL: https://secure-media.collegeboard.org/mSSS/media/pdf/segment-analysis-service-overview.pdf.

[4] T. M. Cottom. Lower ed: The troubling rise of for-profit colleges in the new economy. New Press, The, 2017.

[5] R. Freedle. “Correcting the SAT's ethnic and social-class bias: A method for reestimating SAT scores”. In: Harvard Educational Review 73.1 (2003), pp. 1–43.

[6] D. Hirschman and E. A. Bosk. “Standardizing biases: Selection devices and the quantification of race”. In: Sociology of Race and Ethnicity 6.3 (2020), pp. 348-364. DOI: 10.1177/2332649219844797. URL: https://journals.sagepub.com/doi/abs/10.1177/2332649219844797.

[7] M. M. Holland. Divergent paths to college: Race, class, and inequality in high schools. Rutgers University Press, 2019. DOI: 10.36019/9780813590288.

[8] J. Howell, M. H. Hurwitz, Z. Mabel, et al. Participation in student search service is associated with higher college enrollment and completion. Tech. rep. College Board, 2021. URL: https://cbsearch.collegeboard.org/pdf/college-outreach-and-student-outcomes.pdf.

[9] C. M. Hoxby. How the changing market structure of U.S. higher education explains college tuition. Tech. rep. Working Paper 6323. National Bureau of Economic Research, 1997. URL: http://www.nber.org/papers/W6323.pdf.

[10] C. M. Hoxby. “The changing selectivity of American colleges”. In: Journal of Economic Perspectives 23.4 (2009), pp. 95-118. ISSN: 0895-3309. URL: <Go to ISI>://000272677100005.

[11] S. R. Khan. Privilege: The making of an adolescent elite at St. Paul's School. Princeton studies in cultural sociology. Princeton, N.J.: Princeton University Press, 2011, p. 232. ISBN: 9780691145280 (alk. paper) 0691145288 (alk. paper) 9780691156231 (pbk.) 0691156239 (pbk.).

[12] A. Leyshon and N. Thrift. “Lists come alive: electronic systems of knowledge and the rise of credit-scoring in retail banking”. In: Economy and Society 28.3 (1999), pp. 434-466. ISSN: 0308-5147. DOI: 10.1080/03085149900000013. URL: https://doi.org/10.1080/03085149900000013.

[13] D. Norris. “Embedding racism: City government credit ratings and the institutionalization of race in markets”. In: Social Problems (2021). ISSN: 0037-7791. DOI: 10.1093/socpro/spab066. URL: https://doi.org/10.1093/socpro/spab066.

[14] C. O'Neil. Weapons of math destruction: how big data increases inequality and threatens democracy. First edition.. New York: Crown, 2016, p. x, 259 pages. ISBN: 9780553418811 (hardcover) 9780553418835 (softcover).

[15] A. Posecznick. Selling hope and college merit, markets, and recruitment in an unranked school. Ithaca ; London: Cornell University Press, 2017. ISBN: 9781501707582 9781501709821. URL: https://muse.jhu.edu/book/52616.

[16] S. F. Reardon, D. Kalogrides, and K. Shores. “The geography of racial/ethnic test score gaps”. In: American Journal of Sociology 124.4 (2019), pp. 1164-1221. DOI: 10.1086/700678. URL: https://www.journals.uchicago.edu/doi/abs/10.1086/700678.

[17] A. Rodriguez and E. Hernandez-Hamed. “Understanding unfulfilled AP potential across the participation pipeline by race and income”. In: Teachers College Record 122.9 (2020), p. 38. ISSN: 0161-4681. URL: <Go to ISI>://WOS:000595649100009.

[18] K. G. Salazar. “Recruitment Redlining by Public Research Universities in the Los Angeles and Dallas Metropolitan Areas”. In: The Journal of Higher Education (2022), pp. 1–37. DOI: 10.1080/00221546.2021.2004811. URL: https://doi.org/10.1080/00221546.2021.2004811.

[19] K. G. Salazar, O. Jaquette, and C. Han. “Coming Soon to a Neighborhood Near You? Off-Campus Recruiting by Public Research Universities”. In: American Educational Research Journal 58.6 (2021), pp. 1270-1314. DOI: 10.3102/00028312211001810. URL: https://journals.sagepub.com/doi/abs/10.3102/00028312211001810.

[20] K. G. Salazar, O. Jaquette, and C. Han. Geodemographics of student list purchases: a first look. Tech. rep. Washington, DC: TICAS, 2022. URL: https://ticas.org/wp-content/uploads/2022/09/Geodemographics-of-Student-List-Purchases_A-First-Look.pdf.

[21] M. V. Santelices and M. Wilson. “Unfair Treatment? the case of Freedle, the SAT, and the standardization approach to differential item functioning”. In: Harvard Educational Review 80.1 (2010), pp. 106–133.

[22] J. Smith, J. Howell, and M. Hurwitz. “The impact of college outreach on high schoolers’ college choices: Results from over one thousand natural experiments”. In: Education Finance and Policy 17.1 (2022), pp. 105-128. ISSN: 1557-3060. DOI: 10.1162/edfp_a_00334. URL: https://doi.org/10.1162/edfp_a_00334.

[23] M. L. Stevens. Creating a class: College admissions and the education of elites. Cambridge, MA: Harvard University Press, 2007, p. 308. ISBN: 9780674026735 (alk. paper) 067402673X (alk. paper).

[24] M. J. N. Tiako, E. South, and V. Ray. “Medical Schools as Racialized Organizations: A Primer”. In: Annals of Internal Medicine 174.8 (2021), pp. 1143-1144. DOI:

emraresearch.org